Mobile Robot Localization

Mobile robot localization focuses on accurately determining a robot's position within its environment, crucial for autonomous navigation. Current research emphasizes robust methods that fuse data from diverse sensors (GNSS, IMUs, LiDAR, cameras) using techniques like particle filters, factor graphs, and neural networks (e.g., triplet convolutional networks), often incorporating map information or fiducial markers to improve accuracy and reliability. These advancements are improving the precision and resilience of robot localization, impacting fields like autonomous driving, indoor robotics, and search and rescue operations by enabling more reliable and safe robot navigation in complex and dynamic environments.